PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic pr...

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Main Authors: Apiwat Sangphukieo, Teeraphan Laomettachit, Marasri Ruengjitchatchawalya
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0248682
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author Apiwat Sangphukieo
Teeraphan Laomettachit
Marasri Ruengjitchatchawalya
author_facet Apiwat Sangphukieo
Teeraphan Laomettachit
Marasri Ruengjitchatchawalya
author_sort Apiwat Sangphukieo
collection DOAJ
description A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/photomod.
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spelling doaj.art-a9ea46db7cd04c1ab8e8f986501ff4a42022-12-21T20:46:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024868210.1371/journal.pone.0248682PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.Apiwat SangphukieoTeeraphan LaomettachitMarasri RuengjitchatchawalyaA new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/photomod.https://doi.org/10.1371/journal.pone.0248682
spellingShingle Apiwat Sangphukieo
Teeraphan Laomettachit
Marasri Ruengjitchatchawalya
PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
PLoS ONE
title PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
title_full PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
title_fullStr PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
title_full_unstemmed PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
title_short PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.
title_sort photomodplus a web server for photosynthetic protein prediction from genome neighborhood features
url https://doi.org/10.1371/journal.pone.0248682
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AT teeraphanlaomettachit photomodplusawebserverforphotosyntheticproteinpredictionfromgenomeneighborhoodfeatures
AT marasriruengjitchatchawalya photomodplusawebserverforphotosyntheticproteinpredictionfromgenomeneighborhoodfeatures